Advanced cybersecurity threat mitigation using software supply chain analysis

ABSTRACT

A system and method for comprehensive cybersecurity threat assessment of software applications based on the totality of vulnerabilities from all levels of the software supply chain. The system and method comprising analyzing the code and/or operation of a software application to determine components comprising the software, identifying the source of such components, determining vulnerabilities associated with those components, compiling a list of such components, creating a directed graph of relationships between the components and their sources, and evaluating the overall threat associated with the software application based its software supply chain vulnerabilities.

CROSS-REFERENCE TO RELATED APPLICATIONS

application Ser. No. Date Filed Title Current Herewith HOLISTIC COMPUTER SYSTEM application CYBERSECURITY EVALUATION Ser. No. AND SCORING Is a continuation-in-part of: 16/836,717 Mar. 31, 2020 HOLISTIC COMPUTER SYSTEM CYBERSECURITY EVALUATION AND SCORING which is a continuation-in-part of: 15/887,496 Feb. 2, 2018 SYSTEM AND METHODS FOR SANDBOXED MALWARE ANALYSIS AND AUTOMATED PATCH DEVELOPMENT, DEPLOYMENT AND VALIDATION which is a continuation-in-part of: 15/818,733 Nov. 20, 2017 SYSTEM AND METHOD FOR CYBERSECURITY ANALYSIS AND SCORE GENERATION FOR INSURANCE PURPOSES which is a continuation-in-part of: 15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCED U.S. Pat. No. Issue Date CYBERSECURITY THREAT 10,609,079 Mar. 31, 2020 MITIGATION TO ROGUE DEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY AND PATCH MANAGEMENT which is a continuation-in-part of: 15/655,113 Jul. 20, 2017 ADVANCED CYBERSECURITY THREAT MITIGATION USING BEHAVIORAL AND DEEP ANALYTICS which is a continuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR-DRIVEN DISTRIBUTED COMPUTATIONAL GRAPH and is also a continuation-in-part of: 15/237,625 Aug. 15, 2016 DETECTION MITIGATION U.S. Pat. No. Issue Date AND REMEDIATION OF 10,248,910 Apr. 2, 2019 CYBERATTACKS EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM which is a continuation-in-part of: 15/206,195 Jul. 8, 2016 ACCURATE AND DETAILED MODELING OF SYSTEMS WITH LARGE COMPLEX DATASETS USING A DISTRIBUTED SIMULATION ENGINE which is a continuation-in-part of: 15/186,453 Jun. 18, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION which is a continuation-in-part of: 15/166,158 May 26, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR SECURITY AND CLIENT-FACING INFRASTRUCTURE RELIABILITY which is a continuation-in-part of: 15/141,752 Apr. 28, 2016 SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESS INFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION which is a continuation-in-part of: 15/091,563 Apr. 5, 2016 SYSTEM FOR CAPTURE, U.S. Pat. No. Issued Date ANALYSIS AND STORAGE OF 10,204,147 Feb. 12, 2019 TIME SERIES DATA FROM SENSORS WITH HETEROGENEOUS REPORT INTERVAL PROFILES and is also a continuation-in-part of: 14/986,536 Dec. 31, 2015 DISTRIBUTED SYSTEM FOR U.S. Pat. No. Issued Date LARGE VOLUME DEEP WEB 10,210,255 Feb. 19, 2019 DATA EXTRACTION and is also a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH Current Herewith ADVANCED CYBERSECURITY application THREAT MITIGATION USING Ser. No. SOFTWARE SUPPLY CHAIN ANALYSIS Is a continuation-in-part of: 16/836,717 Mar. 31, 2020 HOLISTIC COMPUTER SYSTEM CYBERSECURITY EVALUATION AND SCORING which is a continuation-in-part of: 15/887,496 Feb. 2, 2018 SYSTEM AND METHODS FOR SANDBOXED MALWARE ANALYSIS AND AUTOMATED PATCH DEVELOPMENT, DEPLOYMENT AND VALIDATION which is a continuation-in-part of: 15/823,285 Nov. 27, 2017 META-INDEXING, SEARCH, COMPLIANCE, AND TEST FRAMEWORK FOR SOFTWARE DEVELOPMENT which is a continuation-in-part of: 15/788,718 Oct. 19, 2017 DATA MONETIZATION AND EXCHANGE PLATFORM which claims priority, and benefit to: 62/568,307 Oct. 4, 2017 DATA MONETIZATION AND EXCHANGE PLATFORM and is also a continuation-in-part of: 15/788,002 Oct. 19, 2017 ALGORITHM MONETIZATION AND EXCHANGE PLATFORM which claims priority, and benefit to: 62/568,305 Oct. 4, 2017 ALGORITHM MONETIZATION AND EXCHANGE PLATFORM and is also a continuation-in-part of: 15/787,601 Oct. 18, 2017 METHOD AND APPARATUS FOR CROWDSOURCED DATA GATHERING, EXTRACTION, AND COMPENSATION which claims priority, and benefit to: 62/568,312 Oct. 4, 2017 METHOD AND APPARATUS FOR CROWDSOURCED DATA GATHERING, EXTRACTION, AND COMPENSATION and is also a continuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR-DRIVEN DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPHY Current Herewith ADVANCED CYBERSECURITY application THREAT MITIGATION USING Ser. No. SOFTWARE SUPPLY CHAIN ANALYSIS Is a continuation-in-part of: 16/777,270 Jan. 30, 2020 CYBERSECURITY PROFILING AND RATING USING ACTIVE AND PASSIVE EXTERNAL RECONNAISSANCE which is a continuation-in-part of: 16/720,383 Dec. 19, 2019 RATING ORGANIZATION CYBERSECURITY USING ACTIVE AND PASSIVE EXTERNAL RECONNAISSANCE which is a continuation of: 15/823,363 Nov. 27, 2017 RATING ORGANIZATION CYBERSECURITY USING ACTIVE AND PASSIVE EXTERNAL RECONNAISSANCE which is a continuation-in-part of: 15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCED CYBERSECURITY THREAT MITIGATION TO ROGUE DEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY AND PATCH MANAGEMENT the entire specification of each of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The disclosure relates to the field of computer management, and more particularly to the field of cybersecurity and threat analytics.

Discussion of the State of the Art

Analysis of the cybersecurity of software has tended to focus on the software application itself, but has largely ignored cybersecurity risks associated with other links in the software development supply chain. Modern software development relies on incorporation of many components upstream of the development of the software application and is subject to vulnerabilities associated with dependencies or data from downstream of the application. The overall cybersecurity risks of a software application, therefore, depends on a complex chain of vulnerabilities that may be introduced at many stages of the software development supply chain. There is currently no system or method for assessing the complexity of these incorporations and dependencies and their impact on the cybersecurity of a software application and its use.

What is needed is a system and method for identifying, tracing, and analyzing each component or service that is incorporated into, contributes to, or is used by a software application from all stages of the software supply chain, such that an comprehensive assessment of all cybersecurity threats associated with a software application can be made.

SUMMARY OF THE INVENTION

Accordingly, the inventor has developed, and reduced to practice, a system and method for comprehensive cybersecurity threat assessment of software applications based on the totality of vulnerabilities from all levels of the software supply chain. The system and method comprising analyzing the code and/or operation of a software application to determine components comprising the software, identifying the source of such components, determining vulnerabilities associated with those components, compiling a list of such components, creating a directed graph of relationships between the components and their sources, and evaluating the overall threat associated with the software application based its software supply chain vulnerabilities.

According to a preferred embodiment, a system for analyzing the cybersecurity threat of software applications from the software supply chain is disclosed, comprising: a computing device comprising a memory and a processor; a software analyzer comprising a first plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive a software application for analysis; identify one or more software components comprising the software application; and send a component identifier for each software component identified to a reconnaissance engine; a reconnaissance engine comprising a second plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to: receive the component identifier for the one or more software components; search one or more databases to identify a source of each software component; search one or more databases to identify a vulnerability of each software component; send the component identifier, source, and vulnerability for each of the one or more software components to a cyber-physical graph engine; a cyber-physical graph engine comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to: receive the component identifier, source, and vulnerability for each of the one or more software components; and construct a cyber-physical graph of a software supply chain for the software application, the cyber-physical graph comprising nodes representing the source and vulnerability of each software component of the software application and edges representing the relationships between the nodes; and a scoring engine comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to: run one or more graph-processing algorithms on the cyber-physical graph to determine one or more paths of vulnerability in the software supply chain and a probability of occurrence for each path; and generate a cybersecurity score for the software application based on the vulnerabilities in the software supply chain.

According to another preferred embodiment, a method for analyzing the cybersecurity threat of software applications from the software supply chain is disclosed, comprising the steps of: receiving a software application for analysis; identifying one or more software components comprising the software application; searching one or more databases to identify a source of each software component; searching one or more databases to identify a vulnerability of each software component; constructing a cyber-physical graph of a software supply chain for the software application, the cyber-physical graph comprising nodes representing the source and vulnerability of each software component of the software application and edges representing the relationships between the nodes; running one or more graph-processing algorithms on the cyber-physical graph to determine one or more paths of vulnerability in the software supply chain and a probability of occurrence for each path; and generating a cybersecurity score for the software application based on the vulnerabilities in the software supply chain.

According to an aspect of an embodiment, one of the databases used to identify a dependency of each software component is a software dependency database containing structured information.

According to an aspect of an embodiment, one of the databases used to identify a vulnerability of each software component is a vulnerability information database containing structured information.

According to an aspect of an embodiment, a natural language processing engine is used to: retrieve structured data from a source of vulnerability information; retrieve unstructured data from a different source of vulnerability information; extract identifiable information regarding vulnerabilities from the structured data; search, identify, and tag the unstructured data using the identifiable information from the structured data, thereby converting the unstructured data to newly structured data; and storing a database comprising the newly structured data; wherein one of the databases used to identify a vulnerability of each software component is the database comprising the newly structured data, or one of the databases used to identify a source of each software component is the database comprising the newly structured data, or both.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way.

FIG. 1 is a block diagram of an exemplary system architecture for an advanced cyber decision platform.

FIG. 2 is a block diagram of an advanced cyber decision platform in an exemplary configuration for use in investment vehicle management.

FIG. 3 is a process diagram showing advanced cyber decision platform functions in use to mitigate cyberattacks.

FIG. 4 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties.

FIG. 5 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect.

FIG. 6 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect.

FIG. 7 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph, according to one aspect.

FIG. 8 is a flow diagram of an exemplary method for cybersecurity behavioral analytics, according to one aspect.

FIG. 9 is a flow diagram of an exemplary method for measuring the effects of cybersecurity attacks, according to one aspect.

FIG. 10 is a flow diagram of an exemplary method for continuous cybersecurity monitoring and exploration, according to one aspect.

FIG. 11 is a flow diagram of an exemplary method for mapping a cyber-physical system graph, according to one aspect.

FIG. 12 is a flow diagram of an exemplary method for continuous network resilience scoring, according to one aspect.

FIG. 13 is a flow diagram of an exemplary method for cybersecurity privilege oversight, according to one aspect.

FIG. 14 is a flow diagram of an exemplary method for cybersecurity risk management, according to one aspect.

FIG. 15 is a flow diagram of an exemplary method for mitigating compromised credential threats, according to one aspect.

FIG. 16 is a flow diagram of an exemplary method for dynamic network and rogue device discovery, according to one aspect.

FIG. 17 is a flow diagram of an exemplary method for Kerberos “golden ticket” attack detection, according to one aspect.

FIG. 18 is a flow diagram of an exemplary method for risk-based vulnerability and patch management, according to one aspect.

FIG. 19 is block diagram showing an exemplary system architecture for a system for cybersecurity profiling and rating.

FIG. 20 is a relational diagram showing the relationships between exemplary 3rd party search tools, search tasks that can be generated using such tools, and the types of information that may be gathered with those tasks.

FIG. 21 is a block diagram showing an exemplary architecture for a software analyzer for a holistic computer system cybersecurity evaluation and scoring system.

FIG. 22 is a block diagram showing exemplary elements of a software supply chain with upstream and downstream sources of cybersecurity vulnerabilities.

FIG. 23 is an exemplary cyber-physical graph showing a software supply chain represented as a directed graph with identification of the sources of specific software components and possible vulnerabilities.

FIG. 24 is a block diagram showing an overall system architecture for a supply chain vulnerability analysis system.

FIG. 25 is a block diagram showing an exemplary architecture for a natural language processing engine for the extraction and processing of data using natural language processing from labeled, unlabeled, and partially-labeled sources of software vulnerability information.

FIG. 26 is a block diagram illustrating an exemplary hardware architecture of a computing device.

FIG. 27 is a block diagram illustrating an exemplary logical architecture for a client device.

FIG. 28 is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services.

FIG. 29 is another block diagram illustrating an exemplary hardware architecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and method for comprehensive cybersecurity threat assessment of software applications based on the totality of vulnerabilities from all levels of the software supply chain. The system and method comprising analyzing the code and/or operation of a software application to determine components comprising the software, identifying the source of such components, determining vulnerabilities associated with those components, compiling a list of such components, creating a directed graph of relationships between the components and their sources, and evaluating the overall threat associated with the software application based its software supply chain vulnerabilities. The system and method may further contain a natural language processing engine which receives structured and unstructured data from one or more sources of vulnerability information, and uses entity recognition and labeling information contained in the structured data to search, identify, and tag information in the unstructured information, so that it can be reorganized into structured information and used as a database in analyzing software supply chain vulnerabilities.

One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements.

Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way.

Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical.

A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence.

When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article.

The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other aspects need not include the device itself.

Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.

Definitions

As used herein, “graph” is a representation of information and relationships, where each primary unit of information makes up a “node” or “vertex” of the graph and the relationship between two nodes makes up an edge of the graph. Nodes can be further qualified by the connection of one or more descriptors or “properties” to that node. For example, given the node “James R,” name information for a person, qualifying properties might be “183 cm tall”, “DOB Aug. 13, 1965” and “speaks English”. Similar to the use of properties to further describe the information in a node, a relationship between two nodes that forms an edge can be qualified using a “label”. Thus, given a second node “Thomas G,” an edge between “James R” and “Thomas G” that indicates that the two people know each other might be labeled “knows.” When graph theory notation (Graph=(Vertices, Edges)) is applied this situation, the set of nodes are used as one parameter of the ordered pair, V and the set of 2 element edge endpoints are used as the second parameter of the ordered pair, E. When the order of the edge endpoints within the pairs of E is not significant, for example, the edge James R, Thomas G is equivalent to Thomas G, James R, the graph is designated as “undirected.” Under circumstances when a relationship flows from one node to another in one direction, for example James R is “taller” than Thomas G, the order of the endpoints is significant. Graphs with such edges are designated as “directed.” In the distributed computational graph system, transformations within transformation pipeline are represented as directed graph with each transformation comprising a node and the output messages between transformations comprising edges. Distributed computational graph stipulates the potential use of non-linear transformation pipelines which are programmatically linearized. Such linearization can result in exponential growth of resource consumption. The most sensible approach to overcome possibility is to introduce new transformation pipelines just as they are needed, creating only those that are ready to compute. Such method results in transformation graphs which are highly variable in size and node, edge composition as the system processes data streams. Those familiar with the art will realize that transformation graph may assume many shapes and sizes with a vast topography of edge relationships. The examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention

As used herein, “transformation” is a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as a example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system. Historically, transformation pipelines have been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration. Other pipeline configurations are possible. The invention is designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical.

A “database” or “data storage subsystem” (these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing, and retrieval of data, the retrieval typically being via some sort of querying interface or language. “Database” may be used to refer to relational database management systems known in the art, but should not be considered to be limited to such systems. Many alternative database or data storage system technologies have been, and indeed are being, introduced in the art, including but not limited to distributed non-relational data storage systems such as Hadoop, column-oriented databases, in-memory databases, and the like. While various aspects may preferentially employ one or another of the various data storage subsystems available in the art (or available in the future), the invention should not be construed to be so limited, as any data storage architecture may be used according to the aspects. Similarly, while in some cases one or more particular data storage needs are described as being satisfied by separate components (for example, an expanded private capital markets database and a configuration database), these descriptions refer to functional uses of data storage systems and do not refer to their physical architecture. For instance, any group of data storage systems of databases referred to herein may be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines as is known in the art. Similarly, any single database (such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or more messaging systems known in the art, or in a master/slave arrangement common in the art. These examples should make clear that no particular architectural approaches to database management is preferred according to the invention, and choice of data storage technology is at the discretion of each implementer, without departing from the scope of the invention as claimed.

A “data context”, as used herein, refers to a set of arguments identifying the location of data. This could be a Rabbit queue, a .csv file in cloud-based storage, or any other such location reference except a single event or record. Activities may pass either events or data contexts to each other for processing. The nature of a pipeline allows for direct information passing between activities, and data locations or files do not need to be predetermined at pipeline start.

A “pipeline”, as used herein and interchangeably referred to as a “data pipeline” or a “processing pipeline”, refers to a set of data streaming activities and batch activities. Streaming and batch activities can be connected indiscriminately within a pipeline. Events will flow through the streaming activity actors in a reactive way. At the junction of a streaming activity to batch activity, there will exist a StreamBatchProtocol data object. This object is responsible for determining when and if the batch process is run. One or more of three possibilities can be used for processing triggers: regular timing interval, every N events, or optionally an external trigger. The events are held in a queue or similar until processing. Each batch activity may contain a “source” data context (this may be a streaming context if the upstream activities are streaming), and a “destination” data context (which is passed to the next activity). Streaming activities may have an optional “destination” streaming data context (optional meaning: caching/persistence of events vs. ephemeral), though this should not be part of the initial implementation.

Conceptual Architecture

FIG. 1 is a block diagram of an advanced cyber decision platform. Client access to the system 105 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, occurs through the system's distributed, extensible high bandwidth cloud interface 110 which uses a versatile, robust web application driven interface for both input and display of client-facing information via network 107 and operates a data store 112 such as, but not limited to MONGODB™, COUCHDB™, CASSANDRA™ or REDIS™ according to various arrangements. Much of the business data analyzed by the system both from sources within the confines of the client business, and from cloud based sources, also enter the system through the cloud interface 110, data being passed to the connector module 135 which may possess the API routines 135 a needed to accept and convert the external data and then pass the normalized information to other analysis and transformation components of the system, the directed computational graph module 155, high volume web crawler module 115, multidimensional time series database (MDTSDB) 120 and the graph stack service 145. The directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information. Within the directed computational graph module 155, data may be split into two identical streams in a specialized pre-programmed data pipeline 155 a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data is then transferred to the general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis. The directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. The high volume web crawling module 115 uses multiple server hosted preprogrammed web spiders, which while autonomously configured are deployed within a web scraping framework 115 a of which SCRAPY™ is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology. The multiple dimension time series data store module 120 may receive streaming data from a large plurality of sensors that may be of several different types. The multiple dimension time series data store module may also store any time series data encountered by the system such as but not limited to enterprise network usage data, component and system logs, performance data, network service information captures such as, but not limited to news and financial feeds, and sales and service related customer data. The module is designed to accommodate irregular and high volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data. Inclusion of programming wrappers 120 a for languages examples of which are, but not limited to C++, PERL, PYTHON, and ERLANG™ allows sophisticated programming logic to be added to the default function of the multidimensional time series database 120 without intimate knowledge of the core programming, greatly extending breadth of function. Data retrieved by the multidimensional time series database (MDTSDB) 120 and the high volume web crawling module 115 may be further analyzed and transformed into task optimized results by the directed computational graph 155 and associated general transformer service 150 and decomposable transformer service 160 modules. Alternately, data from the multidimensional time series database and high volume web crawling modules may be sent, often with scripted cuing information determining important vertexes 145 a, to the graph stack service module 145 which, employing standardized protocols for converting streams of information into graph representations of that data, for example, open graph internet technology although the invention is not reliant on any one standard. Through the steps, the graph stack service module 145 represents data in graphical form influenced by any pre-determined scripted modifications 145 a and stores it in a graph-based data store 145 b such as GIRAPH™ or a key value pair type data store REDIS™, or RIAK™, among others, all of which are suitable for storing graph-based information.

Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the already available data in the automated planning service module 130 which also runs powerful information theory 130 a based predictive statistics functions and machine learning algorithms to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. The using all available data, the automated planning service module 130 may propose business decisions most likely to result is the most favorable business outcome with a usably high level of certainty. Closely related to the automated planning service module in the use of system derived results in conjunction with possible externally supplied additional information in the assistance of end user business decision making, the action outcome simulation module 125 with its discrete event simulator programming module 125 a coupled with the end user facing observation and state estimation service 140 which is highly scriptable 140 b as circumstances require and has a game engine 140 a to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data.

When performing external reconnaissance via a network 107, web crawler 115 may be used to perform a variety of port and service scanning operations on a plurality of hosts. This may be used to target individual network hosts (for example, to examine a specific server or client device) or to broadly scan any number of hosts (such as all hosts within a particular domain, or any number of hosts up to the complete IPv4 address space). Port scanning is primarily used for gathering information about hosts and services connected to a network, using probe messages sent to hosts that prompt a response from that host. Port scanning is generally centered around the transmission control protocol (TCP), and using the information provided in a prompted response a port scan can provide information about network and application layers on the targeted host.

Port scan results can yield information on open, closed, or undetermined ports on a target host. An open port indicated that an application or service is accepting connections on this port (such as ports used for receiving customer web traffic on a web server), and these ports generally disclose the greatest quantity of useful information about the host. A closed port indicates that no application or service is listening for connections on that port, and still provides information about the host such as revealing the operating system of the host, which may discovered by fingerprinting the TCP/IP stack in a response. Different operating systems exhibit identifiable behaviors when populating TCP fields, and collecting multiple responses and matching the fields against a database of known fingerprints makes it possible to determine the OS of the host even when no ports are open. An undetermined port is one that does not produce a requested response, generally because the port is being filtered by a firewall on the host or between the host and the network (for example, a corporate firewall behind which all internal servers operate).

Scanning may be defined by scope to limit the scan according to two dimensions, hosts and ports. A horizontal scan checks the same port on multiple hosts, often used by attackers to check for an open port on any available hosts to select a target for an attack that exploits a vulnerability using that port. This type of scan is also useful for security audits, to ensure that vulnerabilities are not exposed on any of the target hosts. A vertical scan defines multiple ports to examine on a single host, for example a “vanilla scan” which targets every port of a single host, or a “strobe scan” that targets a small subset of ports on the host. This type of scan is usually performed for vulnerability detection on single systems, and due to the single-host nature is impractical for large network scans. A block scan combines elements of both horizontal and vertical scanning, to scan multiple ports on multiple hosts. This type of scan is useful for a variety of service discovery and data collection tasks, as it allows a broad scan of many hosts (up to the entire Internet, using the complete IPv4 address space) for a number of desired ports in a single sweep.

Large port scans involve quantitative research, and as such may be treated as experimental scientific measurement and are subject to measurement and quality standards to ensure the usefulness of results. To avoid observational errors during measurement, results must be precise (describing a degree of relative proximity between individual measured values), accurate (describing relative proximity of measured values to a reference value), preserve any metadata that accompanies the measured data, avoid misinterpretation of data due to faulty measurement execution, and must be well-calibrated to efficiently expose and address issues of inaccuracy or misinterpretation. In addition to these basic requirements, large volumes of data may lead to unexpected behavior of analysis tools, and extracting a subset to perform initial analysis may help to provide an initial overview before working with the complete data set. Analysis should also be reproducible, as with all experimental science, and should incorporate publicly-available data to add value to the comprehensibility of the research as well as contributing to a “common framework” that may be used to confirm results.

When performing a port scan, web crawler 115 may employ a variety of software suitable for the task, such as Nmap, ZMap, or masscan. Nmap is suitable for large scans as well as scanning individual hosts, and excels in offering a variety of diverse scanning techniques. ZMap is a newer application and unlike Nmap (which is more general-purpose), ZMap is designed specifically with Internet-wide scans as the intent. As a result, ZMap is far less customizable and relies on horizontal port scans for functionality, achieving fast scan times using techniques of probe randomization (randomizing the order in which probes are sent to hosts, minimizing network saturation) and asynchronous design (utilizing stateless operation to send and receive packets in separate processing threads). Masscan uses the same asynchronous operation model of ZMap, as well as probe randomization. In masscan however, a certain degree of statistical randomness is sacrificed to improve computation time for large scans (such as when scanning the entire IPv4 address space), using the BlackRock algorithm. This is a modified implementation of symmetric encryption algorithm DES, with fewer rounds and modulo operations in place of binary ones to allow for arbitrary ranges and achieve faster computation time for large data sets.

Received scan responses may be collected and processed through a plurality of data pipelines 155 a to analyze the collected information. MDTSDB 120 and graph stack 145 may be used to produce a hybrid graph/time-series database using the analyzed data, forming a graph of Internet-accessible organization resources and their evolving state information over time. Customer-specific profiling and scanning information may be linked to CPG graphs (as described below in detail, referring to FIG. 11) for a particular customer, but this information may be further linked to the base-level graph of internet-accessible resources and information. Depending on customer authorizations and legal or regulatory restrictions and authorizations, techniques used may involve both passive, semi-passive and active scanning and reconnaissance.

FIG. 2 is a block diagram of an advanced cyber decision platform in an exemplary configuration for use in investment vehicle management 200. The advanced cyber decision platform 100 previously disclosed in co-pending application Ser. No. 15/141,752 and applied in a role of cybersecurity in co-pending application Ser. No. 15/237,625, when programmed to operate as quantitative trading decision platform, is very well suited to perform advanced predictive analytics and predictive simulations 202 to produce investment predictions. Much of the trading specific programming functions are added to the automated planning service module 130 of the modified advanced cyber decision platform 100 to specialize it to perform trading analytics. Specialized purpose libraries may include but are not limited to financial markets functions libraries 251, Monte-Carlo risk routines 252, numeric analysis libraries 253, deep learning libraries 254, contract manipulation functions 255, money handling functions 256, Monte-Carlo search libraries 257, and quant approach securities routines 258. Pre-existing deep learning routines including information theory statistics engine 259 may also be used. The invention may also make use of other libraries and capabilities that are known to those skilled in the art as instrumental in the regulated trade of items of worth. Data from a plurality of sources used in trade analysis are retrieved, much of it from remote, cloud resident 201 servers through the system's distributed, extensible high bandwidth cloud interface 110 using the system's connector module 135 which is specifically designed to accept data from a number of information services both public and private through interfaces to those service's applications using its messaging service 135 a routines, due to ease of programming, are augmented with interactive broker functions 235, market data source plugins 236, e-commerce messaging interpreters 237, business-practice aware email reader 238 and programming libraries to extract information from video data sources 239.

Other modules that make up the advanced cyber decision platform may also perform significant analytical transformations on trade related data. These may include the multidimensional time series data store 120 with its robust scripting features which may include a distributive friendly, fault-tolerant, real-time, continuous run prioritizing, programming platform such as, but not limited to Erlang/OTP 221 and a compatible but comprehensive and proven library of math functions of which the C⁺⁺ math libraries are an example 222, data formalization and ability to capture time series data including irregularly transmitted, burst data; the GraphStack service 145 which transforms data into graphical representations for relational analysis and may use packages for graph format data storage such as Titan 245 or the like and a highly interface accessible programming interface an example of which may be Akka/Spray, although other, similar, combinations may equally serve the same purpose in this role 246 to facilitate optimal data handling; the directed computational graph module 155 and its distributed data pipeline 155 a supplying related general transformer service module 160 and decomposable transformer module 150 which may efficiently carry out linear, branched, and recursive transformation pipelines during trading data analysis may be programmed with multiple trade related functions involved in predictive analytics of the received trade data. Both possibly during and following predictive analyses carried out by the system, results must be presented to clients 105 in formats best suited to convey the both important results for analysts to make highly informed decisions and, when needed, interim or final data in summary and potentially raw for direct human analysis. Simulations which may use data from a plurality of field spanning sources to predict future trade conditions these are accomplished within the action outcome simulation module 125. Data and simulation formatting may be completed or performed by the observation and state estimation service 140 using its ease of scripting and gaming engine to produce optimal presentation results.

In cases where there are both large amounts of data to be cleansed and formalized and then intricate transformations such as those that may be associated with deep machine learning, first disclosed in ¶067 of co-pending application Ser. No. 14/925,974, predictive analytics and predictive simulations, distribution of computer resources to a plurality of systems may be routinely required to accomplish these tasks due to the volume of data being handled and acted upon. The advanced cyber decision platform employs a distributed architecture that is highly extensible to meet these needs. A number of the tasks carried out by the system are extremely processor intensive and for these, the highly integrated process of hardware clustering of systems, possibly of a specific hardware architecture particularly suited to the calculations inherent in the task, is desirable, if not required for timely completion. The system includes a computational clustering module 280 to allow the configuration and management of such clusters during application of the advanced cyber decision platform. While the computational clustering module is drawn directly connected to specific co-modules of the advanced cyber decision platform these connections, while logical, are for ease of illustration and those skilled in the art will realize that the functions attributed to specific modules of an embodiment may require clustered computing under one use case and not under others. Similarly, the functions designated to a clustered configuration may be role, if not run, dictated. Further, not all use cases or data runs may use clustering.

FIG. 3 is a process diagram showing a general flow 300 of advanced cyber decision platform functions in use to mitigate cyberattacks. Input network data which may include network flow patterns 321, the origin and destination of each piece of measurable network traffic 322, system logs from servers and workstations on the network 323, endpoint data 323 a, any security event log data from servers or available security information and event (SIEM) systems 324, external threat intelligence feeds 324 a, identity or assessment context 325, external network health or cybersecurity feeds 326, Kerberos domain controller or ACTIVE DIRECTORY™ server logs or instrumentation 327 and business unit performance related data 328, among many other possible data types for which the invention was designed to analyze and integrate, may pass into 315 the advanced cyber decision platform 310 for analysis as part of its cyber security function. These multiple types of data from a plurality of sources may be transformed for analysis 311, 312 using at least one of the specialized cybersecurity, risk assessment or common functions of the advanced cyber decision platform in the role of cybersecurity system, such as, but not limited to network and system user privilege oversight 331, network and system user behavior analytics 332, attacker and defender action timeline 333, SIEM integration and analysis 334, dynamic benchmarking 335, and incident identification and resolution performance analytics 336 among other possible cybersecurity functions; value at risk (VAR) modeling and simulation 341, anticipatory vs. reactive cost estimations of different types of data breaches to establish priorities 342, work factor analysis 343 and cyber event discovery rate 344 as part of the system's risk analytics capabilities; and the ability to format and deliver customized reports and dashboards 351, perform generalized, ad hoc data analytics on demand 352, continuously monitor, process and explore incoming data for subtle changes or diffuse informational threads 353 and generate cyber-physical systems graphing 354 as part of the advanced cyber decision platform's common capabilities. Output 317 can be used to configure network gateway security appliances 361, to assist in preventing network intrusion through predictive change to infrastructure recommendations 362, to alert an enterprise of ongoing cyberattack early in the attack cycle, possibly thwarting it but at least mitigating the damage 362, to record compliance to standardized guidelines or SLA requirements 363, to continuously probe existing network infrastructure and issue alerts to any changes which may make a breach more likely 364, suggest solutions to any domain controller ticketing weaknesses detected 365, detect presence of malware 366, and perform one time or continuous vulnerability scanning depending on client directives 367. These examples are, of course, only a subset of the possible uses of the system, they are exemplary in nature and do not reflect any boundaries in the capabilities of the invention.

FIG. 4 is a process flow diagram of a method for segmenting cyberattack information to appropriate corporation parties 400. As previously disclosed 200, 351, one of the strengths of the advanced cyber-decision platform is the ability to finely customize reports and dashboards to specific audiences, concurrently is appropriate. This customization is possible due to the devotion of a portion of the advanced cyber decision platform's programming specifically to outcome presentation by modules which include the observation and state estimation service 140 with its game engine 140 a and script interpreter 140 b. In the setting of cybersecurity, issuance of specialized alerts, updates and reports may significantly assist in getting the correct mitigating actions done in the most timely fashion while keeping all participants informed at predesignated, appropriate granularity. Upon the detection of a cyberattack by the system 401 all available information about the ongoing attack and existing cybersecurity knowledge are analyzed, including through predictive simulation in near real time 402 to develop both the most accurate appraisal of current events and actionable recommendations concerning where the attack may progress and how it may be mitigated. The information generated in totality is often more than any one group needs to perform their mitigation tasks. At this point, during a cyberattack, providing a single expansive and all inclusive alert, dashboard image, or report may make identification and action upon the crucial information by each participant more difficult, therefore the cybersecurity focused arrangement may create multiple targeted information streams each concurrently designed to produce most rapid and efficacious action throughout the enterprise during the attack and issue follow-up reports with and recommendations or information that may lead to long term changes afterward 403. Examples of groups that may receive specialized information streams include but may not be limited to front line responders during the attack 404, incident forensics support both during and after the attack 405, chief information security officer 406 and chief risk officer 407 the information sent to the latter two focused to appraise overall damage and to implement both mitigating strategy and preventive changes after the attack. Front line responders may use the cyber-decision platform's analyzed, transformed and correlated information specifically sent to them 404 a to probe the extent of the attack, isolate such things as: the predictive attacker's entry point onto the enterprise's network, the systems involved or the predictive ultimate targets of the attack and may use the simulation capabilities of the system to investigate alternate methods of successfully ending the attack and repelling the attackers in the most efficient manner, although many other queries known to those skilled in the art are also answerable by the invention. Simulations run may also include the predictive effects of any attack mitigating actions on normal and critical operation of the enterprise's IT systems and corporate users. Similarly, a chief information security officer may use the cyber-decision platform to predictively analyze 406 a what corporate information has already been compromised, predictively simulate the ultimate information targets of the attack that may or may not have been compromised and the total impact of the attack what can be done now and in the near future to safeguard that information. Further, during retrospective forensic inspection of the attack, the forensic responder may use the cyber-decision platform 405 a to clearly and completely map the extent of network infrastructure through predictive simulation and large volume data analysis. The forensic analyst may also use the platform's capabilities to perform a time series and infrastructural spatial analysis of the attack's progression with methods used to infiltrate the enterprise's subnets and servers. Again, the chief risk officer would perform analyses of what information 407 a was stolen and predictive simulations on what the theft means to the enterprise as time progresses. Additionally, the system's predictive capabilities may be employed to assist in creation of a plan for changes of the IT infrastructural that should be made that are optimal for remediation of cybersecurity risk under possibly limited enterprise budgetary constraints in place at the company so as to maximize financial outcome.

FIG. 5 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 500, according to one aspect. According to the aspect, a DCG 500 may comprise a pipeline orchestrator 501 that may be used to perform a variety of data transformation functions on data within a processing pipeline, and may be used with a messaging system 510 that enables communication with any number of various services and protocols, relaying messages and translating them as needed into protocol-specific API system calls for interoperability with external systems (rather than requiring a particular protocol or service to be integrated into a DCG 500).

Pipeline orchestrator 501 may spawn a plurality of child pipeline clusters 502 a-b, which may be used as dedicated workers for streamlining parallel processing. In some arrangements, an entire data processing pipeline may be passed to a child cluster 502 a for handling, rather than individual processing tasks, enabling each child cluster 502 a-b to handle an entire data pipeline in a dedicated fashion to maintain isolated processing of different pipelines using different cluster nodes 502 a-b. Pipeline orchestrator 501 may provide a software API for starting, stopping, submitting, or saving pipelines. When a pipeline is started, pipeline orchestrator 501 may send the pipeline information to an available worker node 502 a-b, for example using AKKA™ clustering. For each pipeline initialized by pipeline orchestrator 501, a reporting object with status information may be maintained. Streaming activities may report the last time an event was processed, and the number of events processed. Batch activities may report status messages as they occur. Pipeline orchestrator 501 may perform batch caching using, for example, an IGFS™ caching filesystem. This allows activities 512 a-d within a pipeline 502 a-b to pass data contexts to one another, with any necessary parameter configurations.

A pipeline manager 511 a-b may be spawned for every new running pipeline, and may be used to send activity, status, lifecycle, and event count information to the pipeline orchestrator 501. Within a particular pipeline, a plurality of activity actors 512 a-d may be created by a pipeline manager 511 a-b to handle individual tasks, and provide output to data services 522 a-d. Data models used in a given pipeline may be determined by the specific pipeline and activities, as directed by a pipeline manager 511 a-b. Each pipeline manager 511 a-b controls and directs the operation of any activity actors 512 a-d spawned by it. A pipeline process may need to coordinate streaming data between tasks. For this, a pipeline manager 511 a-b may spawn service connectors to dynamically create TCP connections between activity instances 512 a-d. Data contexts may be maintained for each individual activity 512 a-d, and may be cached for provision to other activities 512 a-d as needed. A data context defines how an activity accesses information, and an activity 512 a-d may process data or simply forward it to a next step. Forwarding data between pipeline steps may route data through a streaming context or batch context.

A client service cluster 530 may operate a plurality of service actors 521 a-d to serve the requests of activity actors 512 a-d, ideally maintaining enough service actors 521 a-d to support each activity per the service type. These may also be arranged within service clusters 520 a-d, in a manner similar to the logical organization of activity actors 512 a-d within clusters 502 a-b in a data pipeline. A logging service 530 may be used to log and sample DCG requests and messages during operation while notification service 540 may be used to receive alerts and other notifications during operation (for example to alert on errors, which may then be diagnosed by reviewing records from logging service 530), and by being connected externally to messaging system 510, logging and notification services can be added, removed, or modified during operation without impacting DCG 500. A plurality of DCG protocols 550 a-b may be used to provide structured messaging between a DCG 500 and messaging system 510, or to enable messaging system 510 to distribute DCG messages across service clusters 520 a-d as shown. A service protocol 560 may be used to define service interactions so that a DCG 500 may be modified without impacting service implementations. In this manner it can be appreciated that the overall structure of a system using an actor-driven DCG 500 operates in a modular fashion, enabling modification and substitution of various components without impacting other operations or requiring additional reconfiguration.

FIG. 6 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 500, according to one aspect. According to the aspect, a variant messaging arrangement may utilize messaging system 510 as a messaging broker using a streaming protocol 610, transmitting and receiving messages immediately using messaging system 510 as a message broker to bridge communication between service actors 521 a-b as needed. Alternately, individual services 522 a-b may communicate directly in a batch context 620, using a data context service 630 as a broker to batch-process and relay messages between services 522 a-b.

FIG. 7 is a diagram of an exemplary architecture for a system for rapid predictive analysis of very large data sets using an actor-driven distributed computational graph 500, according to one aspect. According to the aspect, a variant messaging arrangement may utilize a service connector 710 as a central message broker between a plurality of service actors 521 a-b, bridging messages in a streaming context 610 while a data context service 630 continues to provide direct peer-to-peer messaging between individual services 522 a-b in a batch context 620.

It should be appreciated that various combinations and arrangements of the system variants described above (referring to FIGS. 1-7) may be possible, for example using one particular messaging arrangement for one data pipeline directed by a pipeline manager 511 a-b, while another pipeline may utilize a different messaging arrangement (or may not utilize messaging at all). In this manner, a single DCG 500 and pipeline orchestrator 501 may operate individual pipelines in the manner that is most suited to their particular needs, with dynamic arrangements being made possible through design modularity as described above in FIG. 5.

FIG. 19 is block diagram showing an exemplary system architecture 1900 for a system for cybersecurity profiling and rating. The system in this example contains a cyber-physical graph 1902 which is used to represent a complete picture of an organization's infrastructure and operations including, importantly, the organization's computer network infrastructure particularly around system configurations that influence cybersecurity protections and resiliency. The system further contains a directed computational graph 1911, which contains representations of complex processing pipelines and is used to control workflows through the system such as determining which 3^(rd) party search tools 1915 to use, assigning search tasks, and analyzing the cyber-physical graph 1902 and comparing results of the analysis against reconnaissance data received from the reconnaissance engine 1906 and stored in the reconnaissance data storage 1905. In some embodiments, the determination of which 3^(rd) party search tools 1915 to use and assignment of search tasks may be implemented by a reconnaissance engine 1906. The cyber-physical graph 1902 plus the analyses of data directed by the directed computational graph on the reconnaissance data received from the reconnaissance engine 1906 are combined to represent the cyber-security profile of the client organization whose network 1907 is being evaluated. A queuing system 1912 is used to organize and schedule the search tasks requested by the reconnaissance engine 1906. A data to rule mapper 1904 is used to retrieve laws, policies, and other rules from an authority database 1903 and compare reconnaissance data received from the reconnaissance engine 1906 and stored in the reconnaissance data storage 1905 against the rules in order to determine whether and to what extent the data received indicates a violation of the rules. Machine learning models 1901 may be used to identify patterns and trends in any aspect of the system, but in this case are being used to identify patterns and trends in the data which would help the data to rule mapper 1904 determine whether and to what extent certain data indicate a violation of certain rules. A scoring engine 1910 receives the data analyses performed by the directed computational graph 1911, the output of the data to rule mapper 1904, plus event and loss data 1914 and contextual data 1909 which defines a context in which the other data are to be scored and/or rated. A public-facing proxy network 1908 is established outside of a firewall 1917 around the client network 1907 both to control access to the client network from the Internet 1913, and to provide the ability to change the outward presentation of the client network 1907 to the Internet 1913, which may affect the data obtained by the reconnaissance engine 1906. In some embodiments, certain components of the system may operate outside the client network 1907 and may access the client network through a secure, encrypted virtual private network (VPN) 1916, as in a cloud-based or platform-as-a-service implementation, but in other embodiments some or all of these components may be installed and operated from within the client network 1907.

As a brief overview of operation, information is obtained about the client network 1907 and the client organization's operations, which is used to construct a cyber-physical graph 1902 representing the relationships between devices, users, resources, and processes in the organization, and contextualizing cybersecurity information with physical and logical relationships that represent the flow of data and access to data within the organization including, in particular, network security protocols and procedures. The directed computational graph 1911 containing workflows and analysis processes, selects one or more analyses to be performed on the cyber-physical graph 1902. Some analyses may be performed on the information contained in the cyber-physical graph, and some analyses may be performed on or against the cyber-physical graph using information obtained from the Internet 1913 from reconnaissance engine 1906. The workflows contained in the directed computational graph 1911 select one or more search tools to obtain information about the organization from the Internet 1915, and may comprise one or more third party search tools 1915 available on the Internet. As data are collected, they are fed into a reconnaissance data storage 1905, from which they may be retrieved and further analyzed. Comparisons are made between the data obtained from the reconnaissance engine 1906, the cyber-physical graph 1902, the data to rule mapper, from which comparisons a cybersecurity profile 1918 of the organization is developed. The cybersecurity profile 1918 is sent to the scoring engine 1910 along with event and loss data 1914 and context data 1909 for the scoring engine 1910 to develop a score and/or rating for the organization that takes into consideration both the cybersecurity profile 1918, context, and other information.

FIG. 21 is a block diagram showing an exemplary architecture for a software analyzer 2100 for a holistic computer system cybersecurity evaluation and scoring system. In this embodiment, the software analyzer 2100 comprises a software definition portal 2110, a source code analyzer 2120, a compiler 2130, a compiled code analyzer 2140, and one or more database resources 2150. The software definition portal 2110 receives either uncompiled, human-readable source code 2111, or compiled machine-readable binary code 2112 to define the software component of the system under test. In this example, definition by specification is not used, and it is assumed that the software to be tested is provided. If source code 2111 is provided, the software definition portal 2110 forwards the source code 2111 to the source code analyzer 2120 for coding analysis prior to compiling.

The source code analyzer 2120 comprises a coding library analyzer 2121 and a coding complexity analyzer 2122. The coding library analyzer 2121 searches the code for functions, classes, modules, routines, system calls, and other portions of code that rely on or incorporate code contained in code libraries developed by a different entity than the entity that developed the software under test. Code libraries are collections of code that have been developed for use in specific circumstances, such as standardized classes developed for an object-oriented coding language (e.g., C++, JAVA, etc.), tools developed for a particular integrated development environment (e.g., Code::Blocks, Eclipse), common libraries for interacting with the operating system, templates, subroutines, etc., that are designed to help speed up, standardize, or make easier the coding of applications. The code in such libraries is of varying quality, complexity, usability, security, etc. Code in open source libraries is particularly variable, depending on the skill and knowledge of the (usually part-time, volunteer) contributors, and subject to deprecation if maintenance of the code slows or stops. The source code analyzer 2121 uses this information to determine which code libraries are used, what code from the libraries is used, and the security level of that code, and the security level of the source code 2111 as a result of using code from those libraries. The coding library analyzer 2121 may access one or more database resources 2150 such as open source libraries 2151 a, malware databases 2151 b, adversary simulations (not shown, but e.g., Cobalt Strike), pen testing tools (not shown, but e.g., Meta sploit), post-exploitation agents (not shown, but e.g., Empire), lists of deprecated or out of date software, etc.

The coding complexity analyzer 2122 analyzes the level of additional cybersecurity risk due to the complexity of the code. As an illustrative example, the cyclomatic complexity of a particular software package is a strong indicator of the number of errors that are likely to be in the code. The cyclomatic complexity of a piece of software is a quantitative measure of the number of linearly independent paths through a program's source code.

After the source code analyzer 2120 has completed analysis of the source code 2111, the source code 2111 is compiled by a compiler 2130 for operational testing. The compiler 2130 used will depend on the language in which the source code 2111 was written. Many different compilers 2130 may be available for any given coding language.

Binary code 2112, whether received directly by the software definition portal 2110 or compiled by the compiler 2130 from source code 2111, is sent to a compiled code analyzer 2140 which analyzes the software while it is in operation (i.e., running) on hardware under an operating system. While the software is running, a function extractor 2141 monitors which operations are performed by the software, the order of such operations, and which system resources are accessed by the software, which can disclose the functions, subroutines, etc., that are being executed by the compiled code. The characteristics of those functions, subroutines, etc., can be matched to similar functions, subroutines, etc., in coding libraries and such that the function extractor can identify code from code libraries that are contained in, and being used by, the compiled software. This information about the binary code 2112 can be sent to the coding library analyzer 2121 for analysis (typically where such analysis has not already been performed by the source code analyzer 2120). Further, a low-level system access detector 2143 will simultaneously monitor the running software to identify access of, or attempted access of, low-level system resources (e.g., kernel, stack, heap, etc.)

that may indicate cybersecurity concerns. A compiler identifier 2144 can be used to identify the compiler used to create the binary code 2112 and certain information about the settings used when during compilation. In many cases, compilers embed information in the compiled code such as the compiler identification, version number, settings, etc., in a comment section composed of ASCII text. The binary can be scanned for such textual information. Alternatively, the binary file can be “decompiled” or “disassembled” in an attempt to match the inputs and outputs of known compilers. The compiler identifier 2144 may access one or more database resources 2150 to make its determination, such as a database of compilers 2151 n and their identifications. An important aspect of cybersecurity analysis of software is determining whether or not a compiler's safety features were enabled, which is done by the compiler safety feature analyzer 2145. Modern compilers have the ability to substitute insecure functions called for in the source code with more secure versions that perform the same functions. However, if this feature is not enabled, the functions will not be substituted. Enablement of the safety features can be determined using the same methods as for compiler identification. A crash tester 2146 may be used to determine the robustness of the software to bad inputs or attempts to crash or hang the software by intentionally inputting improper or unexpected information. Crash logs and reports may be used to gather data about particular failures or type of failure, such as the system logs created by Windows error reporting, Mac crash reports, and Linux kdump. The data from these crash logs and reports can be used to perform temporal, graph, and temporal-graph analysis to compare and contrast how log data, performance and resource data (e.g. statsd type metrics), network connectivity (e.g. systrace collections) and configuration changes and events, file changes, and software library versions, operating systems, and other factors impact stability, uptime, failure rates and recovery times (e.g. MTTR and MTBF), etc. Further, a functional efficiency evaluator 2147 may be used to evaluate whether the software does what it purports to do, and its level of efficiency in doing so. For example, if the software is a malware detector, the functional efficiency evaluator 2147 may determine whether it functions as such, and evaluate what percentage of malware introduced into the computer system it detects and quarantines.

FIG. 22 is a block diagram showing exemplary elements of a software supply chain 2200 with upstream and downstream sources of cybersecurity vulnerabilities. While not all possible software supply chain elements are shown here, exemplary categories in the software supply chain are shown, such as the language development level 2210, open source class libraries 2220, online service providers 2230, the developer of a software application of interest 2240, the purchaser of a software application of interest 2250, sub-contractors 2260, and lower-tier subcontractors 2270. In this example, risk is being assessed from the level of the purchaser 2250 of the software application of interest, shown as the risk assessment level 2282. Cybersecurity risks associated with the software application from upstream in the supply chain are shown as upstream risk 2281 relative to the risk assessment level 2282, and cybersecurity risks associated with the software application from downstream in the supply chain are shown as downstream risk 2281 relative to the risk assessment level 2282.

Language developers 2210 are developers of coding languages 2211 which are used to code software applications. High-level, object-oriented languages such as JAVA, C++, Python, etc., contain code objects called classes, which contain pre-designed functionality that can be called upon to perform certain pre-defined functions. These classes (usually organized into libraries native to the language) can contain vulnerabilities that can later be discovered and exploited.

Another major upstream source of vulnerabilities that can be incorporated into a software application are open source code libraries 2220, which contain classes designed to add functionality to, or make development easier for, the particular language for which the class was developed 2221. Open source libraries 2220 can be written or changed by anyone, and therefore often contain code in classes with vulnerabilities that go unrecognized for some time after a given class is released. Upon compilation, the classes used from the open source library 2220 are incorporated into the software application, and the vulnerabilities are thus incorporated into the executable binary code.

Online micro-services providers 2230 provide well-maintained, online modules (aka micro-services 2231) that can be linked together to provide more complex functionality. While entire software applications can often be built using micro-services 2231, it is also the case that they can be used to create certain functionality (typically more complex functionality) that may not be available in open source code libraries or may be difficult to implement in a stand-alone software application. A software application can be configured to call on functionality created using micro-services 2231, creating a hybrid between a monolithic software application and micro-service 2231 functionality. As micro-services 2231 are also software implementations, they can have vulnerabilities, which then expose any software application using them to potential cybersecurity threats.

It is assumed in this example that the software developer is the first party in a transaction involving a purchaser, the second party in the transaction, which one or more lower tier subcontractors 2260 (third party), 2270 (fourth party). As with the coding from other upstream sources, coding performed by the software developer can introduce cybersecurity vulnerabilities into the software application in develops 2241. Even where a native class or an open source class contains no vulnerabilities, improper implementation of classes can lead to vulnerabilities at the software developer level. For example, the native string handler class in a given language may operate properly, but a coding error by the software developer may improperly validate (or fail to validate) string inputs, possibly leading to a stack overflow and a crash of the software application, allowing an attacker to access the operating system on the affected computing device.

In this example the purchaser of the software 2250 is the second party in the transaction, having purchased the software application (or software-as-a-service) from the software developer 2240. Users of the application 2251 are the purchaser level 2250 are subject to the risks associated with cybersecurity threats, so the risk assessment level 2282 in this example is set at the purchaser 2250 level, and all upstream risks 2281 and downstream risks 2283 are evaluated from this perspective. However, the risk assessment level 2282 can be at any level of the software supply chain. At the purchaser level 2250, the primary risk introduced into the supply chain is improper use of the application or improper security settings established by the purchaser 2250 or its IT department.

The downstream risks 2283 associated with subcontractors 2260, 2270 are primarily related to application dependencies and data provided to the software application 2261, 2271. The software application may depend on functionality and/or data provided by subcontractors 2260, which may further depend on functionality and/or data provided by lower tier subcontractors 2270. While it is not shown in this simplified example, it is also possible that the functionality and/or data provided by subcontractors 2260, 2270 also uses applications that use open source class libraries 2220 and/or micro-services 2230, in which case the cybersecurity issues for downstream risk 2283 can mirror those for upstream risk 2281.

FIG. 23 is an exemplary cyber-physical graph showing a software supply chain represented as a directed graph with identification of the sources of specific software components and possible vulnerabilities. A cyber-physical graph represents the relationships between entities associated with an organization, for example, devices, users, resources, groups, and computing services, the relationships between the entities defining relationships and processes in an organization's infrastructure, thereby contextualizing security information with physical and logical relationships that represent the flow of data and access to data within the organization including, in particular, network security protocols and procedures. A cyber-physical graph, in its most basic form, is a knowledge graph representing the network devices comprising an organization's network infrastructure as nodes (also called vertices) in the graph and the physical or logical connections between them as edges between the nodes. The cyber-physical graph may be expanded to include network information and processes such as data flow, security protocols and procedures, and software versions and patch information. Further, human users and their access privileges to devices and assets may be included. A cyber-security graph may be further expanded to include internal process information such as business processes, loss information, and legal requirements and documents; external information such as domain and IP information, data breach information; and generated information such as open port information from external network scans, and vulnerabilities and avenues of attack. Thus, a cyber-physical graph may be used to represent a complete picture of an organization's infrastructure and operations.

In the context of this example, the cyber-physical graph represents the relationships between components of a software application, the source of those components, and the vulnerabilities associated with those components. The structure of this cyber-physical graph mirrors the representation of the software supply chain shown in a prior drawing. While not all possible software supply chain elements are shown here, exemplary categories in the software supply chain are shown, such as the language development level 2301, open source code libraries 2302, online service providers 2303, the developer of a software application of interest 2304, the purchaser of a software application of interest 2305, sub-contractors 2306, and lower-tier subcontractors 2307. Components incorporated into, or used by, the software application are represented by nodes in the cyber-physical graph shown here as circles at each level of the supply chain 2301 a, 2302 a-e, 2303 a-d, 2304 a, 2305 a, 2306 a-c, and 2307 a-h, and the relationships between the components are shown as directional edges between the nodes. Vulnerabilities associated with each component may be represented by data within the node for that component or as edge labels (where the vulnerability affects the component or components to which it is attached). The severity of a vulnerability or its effects may be designated by an edge weight.

Data may be obtained for the graph through the use of various means, including but not limited to, self-reported data, internet reconnaissance using 3^(rd) party tools as described in FIG. 19, and software analysis as described in FIG. 21. Further, as will be explained below, natural language processing may be used to extract, organize, and utilize information from archives of structured and unstructured vulnerability data.

In this manner, a comprehensive set of data containing all identified or suspected vulnerabilities associated with all levels of the software supply chain is created. A comprehensive cybersecurity threat assessment based on the totality of vulnerabilities from all levels of the software supply chain may be performed by processing the cyber-physical graph by running graph analysis algorithms such as shortest path algorithms, minimum cost/maximum flow algorithms, strongly connected node algorithms, etc., to identify the probabilities of success of cyberattacks through a given vulnerability and the impact of a successful cyberattack.

It is important to note that this system not only analyzes static code features, but dynamically updates on a periodic or continuous basis to capture cybersecurity risks associated with dynamic effects in the software supply chain. Patches, updates, deprecations, changes to EULAs and other licenses, are monitored and updated as they occur, and changes to the software supply chain are propagated through the cyber-physical graph. The cyber-physical graph is then re-analyzed to identify new or changed vulnerability paths in the software supply chain. Vulnerabilities exceeding certain parameters can be established to trigger warnings, alarms, and alerts to notify administrators of cybersecurity threat/risk levels that exceed the established parameters, and identify precisely which components in the supply chain are causing the threat/risk, so those components can be addressed (e.g., by removing that component, service, etc., from the software application or eliminating its use by the software application).

FIG. 24 is a block diagram showing an overall system architecture 2400 for a supply chain vulnerability analysis system. The system comprises a software analyzer 2401, a cyber-physical graph generator 2402, a software component list generator 2403, a scoring engine 2404, and search tools optionally including one or more 3^(rd) party search tools 2405, and a natural language processing engine 2500. The software analyzer operates in a manner analogous to the software analyzer for the holistic computer system cybersecurity evaluation and scoring system shown in FIG. 21 and described in the accompanying text. It receives a software application in either source code or binary form, analyzes the software application to determine the components that comprise the software application, and the source of those components. The software analyzer 2401 may use tools to search the Internet 2406 to identify the components and the source of those components. The search tools may comprise one or more 3^(rd) party search tools 2405, some of which may be cloud-based services accessible through the Internet 2406. Examples of such search tools and their uses can be found in FIG. 20 and described in the accompanying text. Further, a natural language processing engine 2500 is used to extract, index, and analyze text from legal documents associated with the software components such as end user license agreements (EULAs), product licenses, terms of use, etc., to identify changes in the licensing of, and risks associated with, incorporation or use of certain components into a software application. The natural language processing engine may also be used to extract, index, and analyze text from threat information databases which may contain information associated with the software components such as information and files contained in structured threat information expression (STIX) databases, trusted automated exchange of intelligence information (TAXII) databases, common vulnerabilities and exposures (CVE) databases, etc. A cyber-physical graph generator 2403 is used to create a cyber-physical graph of the components incorporated into or used by the software application, such that analyses may be performed on the graph to estimate risks associated with various paths in the cyber-physical graph, each of which represents a potential chain if vulnerability in the software supply chain for that software application. A software component list generator 2403 creates a list of components that are incorporated into, or used by, the software application, and their sources and potential vulnerabilities. This software component list is analogous to a “bill of materials” for physical goods, which identifies all components incorporated into a physical good, and the source of those components. A scoring engine 2404 may be used to assign a score to the risks identified in the software supply chain for the software application.

The software component list and scoring can be used to provide a verified or certified risk level that can be used in many industries where cybersecurity risk is a concern. One application, in particular, is to certify the level of risk of a software application and its supply chain for purposes of establishing terms and conditions and premium pricing for cyber liability insurance. While not shown here, the system may further comprise a red team/blue team testing module, wherein the software supply chain can be tested by having a red team intentionally introduce vulnerabilities into the software supply chain for a given software application to see if the system properly identifies the vulnerability and reports it to the blue team. This concept can be extended further by including a policy database which links industry compliance requirements (e.g., privacy protection regulations in the finance or health industries) to certifications of cyber-security of software applications and their supply chains to demonstrate compliance with industry compliance regulations. In this manner, the software component list and scoring can server as evidentiary documentation of compliance and adequacy of controls. Finally, using this system, simulations and parametric evaluations can be run to determine and/or predict the effect of certain changes, including deterministic and/or stochastic event sets, and a hypothetical cybersecurity score can be created based on these simulations for each set of conditions.

FIG. 25 is a block diagram showing an exemplary architecture for a natural language processing engine 2500 for the extraction and processing of data using natural language processing from labeled, unlabeled, and partially-labeled sources of software vulnerability information. The natural language processing engine may be used to index and categorize text from any data source, but in this example is used primarily to extract, index, and analyze text from two types of documents, legal documents and threat information databases. With respect legal documents, the natural language processing engine is used to extract, index, and analyze text from legal documents associated with the software components such as end user license agreements (EULAs), product licenses, terms of use, etc., to identify changes in the licensing of, and risks associated with, incorporation or use of certain components into a software application. With respect threat information databases, the natural language processing engine is used is used to extract, index, and analyze text from threat information databases which may contain information associated with the software components such as information and files contained in structured threat information expression (STIX) databases, trusted automated exchange of intelligence information (TAXII) databases, common vulnerabilities and exposures (CVE) databases, etc.

In this embodiment, the natural language processing engine comprises one or more cloud-based storage bins 2501, 2503, 2505, one or more data extraction steps 2502, 2506, a natural language processor 2504, a non-relational database 2507, and a graphical representation service 2508, the operation of which is analogous to the GraphStack service 145, which transforms data into graphical representations for relational analysis and may use available third party packages for graph format data storage such as Janus and/or a highly interface accessible programming interface such as Akka-Http, although other, similar, combinations may equally serve the same purpose in this role to facilitate optimal data handling and visualization.

In this embodiment, cloud-based storage bins 2501, 2503, 2505 (e.g., Amazon S3 storage) are used to store data between processing steps. While cloud-based storage bins are a highly convenient means of utilizing dynamically-scalable storage, any form of suitable storage may be used. Data gathered by search tools (e.g., web crawlers, connector services, third party data feeds, etc.) is stored in a first cloud-based storage bin 2501, from which it is retrieved, and subjected to a first data extraction process 2502. The first data extraction process takes data from structured data sources and processes that data to extract identifiable information such as software names, vendors names, versions, operating systems, etc., for use in recognizing similar information in unstructured data. Examples of structured data sources are structured threat information expression (STIX) databases, trusted automated exchange of intelligence information (TAXII) databases, common vulnerabilities and exposures (CVE) databases, etc. Examples of sources of such structured data are the National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD), which provides structured data in XML format of known vulnerabilities for a variety of domains, and Really Simple Syndication (RSS) feeds from companies such as Microsoft's security advisor RSS feed, which also provides structured data in XML format.

The structured data is then sent to storage in a non-relational database 2507, while the unstructured data is sent stored in a second cloud-based storage bin 2503 for retrieval by a natural language processor 2504. The natural language processor 2504 uses the information extracted from the structured data as training data to search, identify, and tag the unstructured data, thereby converting it to structured data. The tagged data are then stored in a third cloud-based storage bin 2505 for retrieval and use in a second data extraction process 2506. The second data extraction process 2605 processes the tagged data to extract identifiable information such as software names, vendors names, versions, operating systems, etc., as was the case for the structured data in the first data extraction process 2502.

The newly structured data (created from unstructured data) are sent to the database 2507, where they are combined into one or more data stores for querying. These databases may be a single, large database, or may be separated into separate databases (e.g. a vulnerability database, a software dictionary, an exploit database, etc.). The databases may then be processed by a graphical representation service 2508, which transforms data into graphical representations for relational analysis.

Detailed Description of Exemplary Aspects

FIG. 8 is a flow diagram of an exemplary method 800 for cybersecurity behavioral analytics, according to one aspect. According to the aspect, behavior analytics may utilize passive information feeds from a plurality of existing endpoints (for example, including but not limited to user activity on a network, network performance, or device behavior) to generate security solutions. In an initial step 801, a web crawler 115 may passively collect activity information, which may then be processed 802 using a DCG 155 to analyze behavior patterns. Based on this initial analysis, anomalous behavior may be recognized 803 (for example, based on a threshold of variance from an established pattern or trend) such as high-risk users or malicious software operators such as bots. These anomalous behaviors may then be used 804 to analyze potential angles of attack and then produce 805 security suggestions based on this second-level analysis and predictions generated by an action outcome simulation module 125 to determine the likely effects of the change. The suggested behaviors may then be automatically implemented 806 as needed. Passive monitoring 801 then continues, collecting information after new security solutions are implemented 806, enabling machine learning to improve operation over time as the relationship between security changes and observed behaviors and threats are observed and analyzed.

This method 800 for behavioral analytics enables proactive and high-speed reactive defense capabilities against a variety of cyberattack threats, including anomalous human behaviors as well as nonhuman “bad actors” such as automated software bots that may probe for, and then exploit, existing vulnerabilities. Using automated behavioral learning in this manner provides a much more responsive solution than manual intervention, enabling rapid response to threats to mitigate any potential impact. Utilizing machine learning behavior further enhances this approach, providing additional proactive behavior that is not possible in simple automated approaches that merely react to threats as they occur.

FIG. 9 is a flow diagram of an exemplary method 900 for measuring the effects of cybersecurity attacks, according to one aspect. According to the aspect, impact assessment of an attack may be measured using a DCG 155 to analyze a user account and identify its access capabilities 901 (for example, what files, directories, devices or domains an account may have access to). This may then be used to generate 902 an impact assessment score for the account, representing the potential risk should that account be compromised. In the event of an incident, the impact assessment score for any compromised accounts may be used to produce a “blast radius” calculation 903, identifying exactly what resources are at risk as a result of the intrusion and where security personnel should focus their attention. To provide proactive security recommendations through a simulation module 125, simulated intrusions may be run 904 to identify potential blast radius calculations for a variety of attacks and to determine 905 high risk accounts or resources so that security may be improved in those key areas rather than focusing on reactive solutions.

FIG. 10 is a flow diagram of an exemplary method 1000 for continuous cybersecurity monitoring and exploration, according to one aspect. According to the aspect, a state observation service 140 may receive data from a variety of connected systems 1001 such as (for example, including but not limited to) servers, domains, databases, or user directories. This information may be received continuously, passively collecting events and monitoring activity over time while feeding 1002 collected information into a graphing service 145 for use in producing time-series graphs 1003 of states and changes over time. This collated time-series data may then be used to produce a visualization 1004 of changes over time, quantifying collected data into a meaningful and understandable format. As new events are recorded, such as changing user roles or permissions, modifying servers or data structures, or other changes within a security infrastructure, these events are automatically incorporated into the time-series data and visualizations are updated accordingly, providing live monitoring of a wealth of information in a way that highlights meaningful data without losing detail due to the quantity of data points under examination.

FIG. 11 is a flow diagram of an exemplary method 1100 for mapping a cyber-physical system graph (CPG), according to one aspect. According to the aspect, a cyber-physical system graph may comprise a visualization of hierarchies and relationships between devices and resources in a security infrastructure, contextualizing security information with physical device relationships that are easily understandable for security personnel and users. In an initial step 1101, behavior analytics information (as described previously, referring to FIG. 8) may be received at a graphing service 145 for inclusion in a CPG. In a next step 1102, impact assessment scores (as described previously, preferring to FIG. 9) may be received and incorporated in the CPG information, adding risk assessment context to the behavior information. In a next step 1103, time-series information (as described previously, referring to FIG. 10) may be received and incorporated, updating CPG information as changes occur and events are logged. This information may then be used to produce 1104 a graph visualization of users, servers, devices, and other resources correlating physical relationships (such as a user's personal computer or smartphone, or physical connections between servers) with logical relationships (such as access privileges or database connections), to produce a meaningful and contextualized visualization of a security infrastructure that reflects the current state of the internal relationships present in the infrastructure.

FIG. 12 is a flow diagram of an exemplary method 1200 for continuous network resilience scoring, according to one aspect. According to the aspect, a baseline score can be used to measure an overall level of risk for a network infrastructure, and may be compiled by first collecting 1201 information on publicly-disclosed vulnerabilities, such as (for example) using the Internet or common vulnerabilities and exploits (CVE) process. This information may then 1202 be incorporated into a CPG as described previously in FIG. 11, and the combined data of the CPG and the known vulnerabilities may then be analyzed 1203 to identify the relationships between known vulnerabilities and risks exposed by components of the infrastructure. This produces a combined CPG 1204 that incorporates both the internal risk level of network resources, user accounts, and devices as well as the actual risk level based on the analysis of known vulnerabilities and security risks.

FIG. 13 is a flow diagram of an exemplary method 1300 for cybersecurity privilege oversight, according to one aspect. According to the aspect, time-series data (as described above, referring to FIG. 10) may be collected 1301 for user accounts, credentials, directories, and other user-based privilege and access information. This data may then 1302 be analyzed to identify changes over time that may affect security, such as modifying user access privileges or adding new users. The results of analysis may be checked 1303 against a CPG (as described previously in FIG. 11), to compare and correlate user directory changes with the actual infrastructure state. This comparison may be used to perform accurate and context-enhanced user directory audits 1304 that identify not only current user credentials and other user-specific information, but changes to this information over time and how the user information relates to the actual infrastructure (for example, credentials that grant access to devices and may therefore implicitly grant additional access due to device relationships that were not immediately apparent from the user directory alone).

FIG. 14 is a flow diagram of an exemplary method 1400 for cybersecurity risk management, according to one aspect. According to the aspect, multiple methods described previously may be combined to provide live assessment of attacks as they occur, by first receiving 1401 time-series data for an infrastructure (as described previously, in FIG. 10) to provide live monitoring of network events. This data is then enhanced 1402 with a CPG (as described above in FIG. 11) to correlate events with actual infrastructure elements, such as servers or accounts. When an event (for example, an attempted attack against a vulnerable system or resource) occurs 1403, the event is logged in the time-series data 1404, and compared against the CPG 1405 to determine the impact. This is enhanced with the inclusion of impact assessment information 1406 for any affected resources, and the attack is then checked against a baseline score 1407 to determine the full extent of the impact of the attack and any necessary modifications to the infrastructure or policies.

FIG. 15 is a flow diagram of an exemplary method 1500 for mitigating compromised credential threats, according to one aspect. According to the aspect, impact assessment scores (as described previously, referring to FIG. 9) may be collected 1501 for user accounts in a directory, so that the potential impact of any given credential attack is known in advance of an actual attack event. This information may be combined with a CPG 1502 as described previously in FIG. 11, to contextualize impact assessment scores within the infrastructure (for example, so that it may be predicted what systems or resources might be at risk for any given credential attack). A simulated attack may then be performed 1503 to use machine learning to improve security without waiting for actual attacks to trigger a reactive response. A blast radius assessment (as described above in FIG. 9) may be used in response 1504 to determine the effects of the simulated attack and identify points of weakness, and produce a recommendation report 1505 for improving and hardening the infrastructure against future attacks.

FIG. 16 is a flow diagram of an exemplary method 1600 for dynamic network and rogue device discovery, according to one aspect. According to the aspect, an advanced cyber decision platform may continuously monitor a network in real-time 1601, detecting any changes as they occur. When a new connection is detected 1602, a CPG may be updated 1603 with the new connection information, which may then be compared against the network's resiliency score 1604 to examine for potential risk. The blast radius metric for any other devices involved in the connection may also be checked 1605, to examine the context of the connection for risk potential (for example, an unknown connection to an internal data server with sensitive information may be considered a much higher risk than an unknown connection to an externally-facing web server). If the connection is a risk, an alert may be sent to an administrator 1606 with the contextual information for the connection to provide a concise notification of relevant details for quick handling.

FIG. 17 is a flow diagram of an exemplary method 1700 for Kerberos “golden ticket” attack detection, according to one aspect. Kerberos is a network authentication protocol employed across many enterprise networks to enable single sign-on and authentication for enterprise services. This makes it an attractive target for attacks, which can result in persistent, undetected access to services within a network in what is known as a “golden ticket” attack. To detect this form of attack, behavioral analytics may be employed to detect erroneously-issued authentication tickets, whether from incorrect configuration or from an attack. According to the aspect, an advanced cyber decision platform may continuously monitor a network 1701, informing a CPG in real-time of all traffic associated with people, places, devices, or services 1702. Machine learning algorithms detect behavioral anomalies as they occur in real-time 1703, notifying administrators with an assessment of the anomalous event 1704 as well as a blast radius score for the particular event and a network resiliency score to advise of the overall health of the network. By automatically detecting unusual behavior and informing an administrator of the anomaly along with contextual information for the event and network, a compromised ticket is immediately detected when a new authentication connection is made.

FIG. 18 is a flow diagram of an exemplary method 1800 for risk-based vulnerability and patch management, according to one aspect. According to the aspect, an advanced cyber decision platform may monitor all information about a network 1801, including (but not limited to) device telemetry data, log files, connections and network events, deployed software versions, or contextual user activity information. This information is incorporated into a CPG 1802 to maintain an up-to-date model of the network in real-time. When a new vulnerability is discovered, a blast radius score may be assessed 1803 and the network's resiliency score may be updated 1804 as needed. A security alert may then be produced 1805 to notify an administrator of the vulnerability and its impact, and a proposed patch may be presented 1806 along with the predicted effects of the patch on the vulnerability's blast radius and the overall network resiliency score. This determines both the total impact risk of any particular vulnerability, as well as the overall effect of each vulnerability on the network as a whole. This continuous network assessment may be used to collect information about new vulnerabilities and exploits to provide proactive solutions with clear result predictions, before attacks occur.

FIG. 20 is a relational diagram showing the relationships between exemplary 3^(rd) party search tools 1915, search tasks 2010 that can be generated using such tools, and the types of information that may be gathered with those tasks 2011-2014, and how a public-facing proxy network 1908 may be used to influence the search task results. While the use of 3^(rd) party search tools 1915 is in no way required, and proprietary or other self-developed search tools may be used, there are numerous 3^(rd) party search tools 1915 available on the Internet, many of them available for use free of charge, that are convenient for purposes of performing external and internal reconnaissance of an organization's infrastructure. Because they are well-known, they are included here as examples of the types of search tools that may be used and the reconnaissance data that may be gathered using such tools. The search tasks 2010 that may be generated may be classified into several categories. While this category list is by no means exhaustive, several important categories of reconnaissance data are domain and internet protocol (IP) address searching tasks 2011, corporate information searching tasks 2012, data breach searching tasks 2013, and dark web searching tasks 2014. Third party search tools 1915 for domain and IP address searching tasks 2011 include, for example, DNSDumpster, Spiderfoot HX, Shodan, VirusTotal, Dig, Censys, ViewDNS, and CheckDMARC, among others. These tools may be used to obtain reconnaissance data about an organization's server IPs, software, geolocation; open ports, patch/setting vulnerabilities; data hosting services, among other data 2031. Third party search tools 1915 for corporate information searching tasks 2012 include, for example, Bloomberg.com, Wikipedia, SEC.gov, AnnualReports.com, DNB.com, Hunter.io, and MarketVisual, among others. These tools may be used to obtain reconnaissance data about an organization's addresses; corp info; high value target (key employee or key data assets) lists, emails, phone numbers, online presence 2032. Third party search tools 1915 for data breach searching tasks 2013 include, for example, DeHashed, WeLeakInfo, Pastebin, Spiderfoot, and BreachCompilation, among others. These tools may be used to obtain reconnaissance data about an organization's previous data breaches, especially those involving high value targets, and similar data loss information 2033. Third party search tools 1915 for deep web (reports, records, and other documents linked to in web pages, but not indexed in search results . . . estimated to be 90% of available web content) and dark web (websites accessible only through anonymizers such as TOR . . . estimated to be about 6% of available web content) searching tasks 2014 include, for example, Pipl, MyLife, Yippy, SurfWax, Wayback machine, Google Scholar, DuckDuckGo, Fazzle, Not Evil, and Start Page, among others. These tools may be used to obtain reconnaissance data about an organization's lost and stolen data such as customer credit card numbers, stolen subscription credentials, hacked accounts, software tools designed for certain exploits, which organizations are being targeted for certain attacks, and similar information 2034. A public-facing proxy network 1908 may be used to change the outward presentation of the organization's network by conducting the searches through selectable attribution nodes 2021 a-n, which are configurable to present the network to the Internet in different ways such as, but not limited to, presenting the organization network as a commercial IP address, a residential IP address, or as an IP address from a particular country, all of which may influence the reconnaissance data received using certain search tools.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).

Referring now to FIG. 26, there is shown a block diagram depicting an exemplary computing device 10 suitable for implementing at least a portion of the features or functionalities disclosed herein. Computing device 10 may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory. Computing device 10 may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more central processing units (CPU) 12, one or more interfaces 15, and one or more busses 14 (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU 12 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device 10 may be configured or designed to function as a server system utilizing CPU 12, local memory 11 and/or remote memory 16, and interface(s) 15. In at least one aspect, CPU 12 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors 13 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10. In a particular aspect, a local memory 11 (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU 12. However, there are many different ways in which memory may be coupled to system 10. Memory 11 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU 12 may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit.

In one aspect, interfaces 15 are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces 15 may for example support other peripherals used with computing device 10. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 26 illustrates one specific architecture for a computing device 10 for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors 13 may be used, and such processors 13 may be present in a single device or distributed among any number of devices. In one aspect, a single processor 13 handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided. In various aspects, different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block 16 and local memory 11) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory 16 or memories 11, 16 may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.

Because such information and program instructions may be employed to implement one or more systems or methods described herein, at least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like. It should be appreciated that such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVA™ compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computing system. Referring now to FIG. 27, there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system. Computing device 20 includes processors 21 that may run software that carry out one or more functions or applications of aspects, such as for example a client application 24. Processors 21 may carry out computing instructions under control of an operating system 22 such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like. In many cases, one or more shared services 23 may be operable in system 20, and may be useful for providing common services to client applications 24. Services 23 may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21. Input devices 28 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory 25 may be random-access memory having any structure and architecture known in the art, for use by processors 21, for example to run software. Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to FIG. 26). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.

In some aspects, systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to FIG. 28, there is shown a block diagram depicting an exemplary architecture 30 for implementing at least a portion of a system according to one aspect on a distributed computing network. According to the aspect, any number of clients 33 may be provided. Each client 33 may run software for implementing client-side portions of a system; clients may comprise a system 20 such as that illustrated in FIG. 27. In addition, any number of servers 32 may be provided for handling requests received from one or more clients 33. Clients 33 and servers 32 may communicate with one another via one or more electronic networks 31, which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other). Networks 31 may be implemented using any known network protocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services 37 may take place, for example, via one or more networks 31. In various aspects, external services 37 may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications 24 are implemented on a smartphone or other electronic device, client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise's or user's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31. For example, one or more databases 34 may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases 34 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various aspects one or more databases 34 may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and so forth). In some aspects, variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36 and configuration systems 35. Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security 36 or configuration system 35 or approach is specifically required by the description of any specific aspect.

FIG. 29 shows an exemplary overview of a computer system 40 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system 40 without departing from the broader scope of the system and method disclosed herein. Central processor unit (CPU) 41 is connected to bus 42, to which bus is also connected memory 43, nonvolatile memory 44, display 47, input/output (I/O) unit 48, and network interface card (NIC) 53. I/O unit 48 may, typically, be connected to peripherals such as a keyboard 49, pointing device 50, hard disk 52, real-time clock 51, a camera 57, and other peripheral devices. NIC 53 connects to network 54, which may be the Internet or a local network, which local network may or may not have connections to the Internet. The system may be connected to other computing devices through the network via a router 55, wireless local area network 56, or any other network connection. Also shown as part of system 40 is power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46. Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components. For example, various software modules may be implemented for performing various functions in connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications of the various aspects described above. Accordingly, the present invention is defined by the claims and their equivalents. 

What is claimed is:
 1. A system for analyzing the cybersecurity threat of software applications from the software supply chain, comprising: a computing device comprising a memory and a processor; a software analyzer comprising a first plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive a software application for analysis; identify one or more software components comprising the software application; and send a component identifier for each software component identified to a reconnaissance engine; a reconnaissance engine comprising a second plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to: receive the component identifier for the one or more software components; search one or more databases to identify a source of each software component; search one or more databases to identify a vulnerability of each software component; send the component identifier, source, and vulnerability for each of the one or more software components to a cyber-physical graph engine; a cyber-physical graph engine comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to: receive the component identifier, source, and vulnerability for each of the one or more software components; and construct a cyber-physical graph of a software supply chain for the software application, the cyber-physical graph comprising nodes representing the source and vulnerability of each software component of the software application and edges representing the relationships between the nodes; and a scoring engine comprising a third plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to: run one or more graph-processing algorithms on the cyber-physical graph to determine one or more paths of vulnerability in the software supply chain and a probability of occurrence for each path; and generate a cybersecurity score for the software application based on the vulnerabilities in the software supply chain.
 2. The system of claim 1, wherein one of the databases used to identify a source of each software component is a vulnerability information database containing structured information.
 3. The system of claim 1, wherein one of the databases used to identify a vulnerability of each software component is a vulnerability information database containing structured information.
 4. The system of claim 1, further comprising a natural language processing engine comprising a fourth plurality of programming instructions stored in the memory of, and operating on the processor of, the computing device, wherein the fourth plurality of programming instructions, when operating on the processor, cause the computing device to: retrieve structured data from a source of vulnerability information; retrieve unstructured data from a different source of vulnerability information; extract identifiable information regarding vulnerabilities from the structured data; search, identify, and tag the unstructured data using the identifiable information from the structured data, thereby converting the unstructured data to newly structured data; and storing a database comprising the newly structured data; wherein one of the databases used to identify a vulnerability of each software component is the database comprising the newly structured data, or one of the databases used to identify a source of each software component is the database comprising the newly structured data, or both.
 5. A method for analyzing the cybersecurity threat of software applications from the software supply chain, comprising the steps of: receiving a software application for analysis; identifying one or more software components comprising the software application; searching one or more databases to identify a source of each software component; searching one or more databases to identify a vulnerability of each software component; constructing a cyber-physical graph of a software supply chain for the software application, the cyber-physical graph comprising nodes representing the source and vulnerability of each software component of the software application and edges representing the relationships between the nodes; running one or more graph-processing algorithms on the cyber-physical graph to determine one or more paths of vulnerability in the software supply chain and a probability of occurrence for each path; and generating a cybersecurity score for the software application based on the vulnerabilities in the software supply chain.
 6. The method of claim 5, wherein one of the databases used to identify a source of each software component is a vulnerability information database containing structured information.
 7. The method of claim 5, wherein one of the databases used to identify a vulnerability of each software component is a vulnerability information database containing structured information.
 8. The method of claim 5, further comprising the steps of: retrieving structured data from a source of vulnerability information; retrieving unstructured data from a different source of vulnerability information; extracting identifiable information regarding vulnerabilities from the structured data; searching, identifying, and tagging the unstructured data using the identifiable information from the structured data, thereby converting the unstructured data to newly structured data; storing a database comprising the newly structured data; and using the database comprising the newly structured data to identify a vulnerability of each software component, or to identify a source of each software component, or both. 